What Is the Best Way To Integrate Vision AI Into My App?

Integrating Vision AI into applications is primarily an engineering challenge rather than just choosing the right model, as constructing robust infrastructure is key to moving from a prototype to a production system. The decision of whether to run AI models on the client (edge) or server involves weighing factors like latency, privacy, bandwidth, and operational costs, often resulting in a hybrid approach where initial processing occurs on-device with more complex tasks handled by the server. Efficient processing requires optimizing frame sampling rates, typically much lower than 30 FPS, to manage costs and computational resources effectively. Mitigating issues like bounding box drift involves synchronizing inference results with video frames using metadata and possibly employing predictive tracking methods. To prevent bottlenecks, frames should be extracted efficiently using a three-stage pipeline that minimizes delays. Cost considerations for managed cloud APIs like AWS Rekognition highlight the importance of sampling strategies to avoid expensive per-image billing, especially at high frame rates. Handling false positives involves using AI outputs as evidence rather than final judgments, implementing a graduated response to violations, and setting calibrated thresholds to reduce the risk of erroneous bans. Establishing audit trails and monitoring false positives in production are critical for maintaining trust and improving system reliability over time.